Combined regression and classification artificial neural networks for sideslip angle estimation and road condition identification

This paper presents an Artificial Neural Network based algorithm to estimate the sideslip angle of a vehicle in different road conditions. The method is based on two parallel tasks: (a) estimation of the sideslip angle in three road conditions, namely dry, wet and icy, by means of three separate regression networks, and (b) identification of the road condition with a pattern recognition classifier allowing to select the correct estimation output among the three regression network outputs. The regression and classification networks are trained by means of datasets recorded during driving sessions conducted on a high-performance instrumented vehicle in the three road conditions and with different maneuvers and driving cycles. The algorithm has been deployed on the same vehicle to test the performance on the real application. The results are presented in terms of comparison between the estimated angle and its direct measurement obtained by a high precision optical sensor installed onboard. The resulting accuracy of the method is about 98.6%.

[1]  Jyotishman Ghosh,et al.  A Deep Learning based Virtual Sensor for Vehicle Sideslip Angle Estimation: Experimental Results , 2018 .

[2]  Nong Zhang,et al.  Side-slip angle estimation and stability control for a vehicle with a non-linear tyre model and a varying speed , 2015 .

[3]  Cornelia Lex,et al.  Vehicle Side Slip Angle Observation with Road Friction Adaptation , 2017 .

[4]  Andrea Tonoli,et al.  Pattern recognition neural classifier for fall detection in rock climbing , 2019 .

[5]  J. Christian Gerdes,et al.  Integrating Inertial Sensors With Global Positioning System (GPS) for Vehicle Dynamics Control , 2004 .

[6]  David M. Bevly,et al.  Integrating INS Sensors With GPS Measurements for Continuous Estimation of Vehicle Sideslip, Roll, and Tire Cornering Stiffness , 2006, IEEE Transactions on Intelligent Transportation Systems.

[7]  Yi Lu Murphey,et al.  Multi-class pattern classification using neural networks , 2007, Pattern Recognit..

[8]  Juan Antonio Cabrera Carrillo,et al.  Robust Road Condition Detection System Using In-Vehicle Standard Sensors , 2015, Sensors.

[9]  Bei Shaoyi,et al.  Vehicle Sideslip Angle Estimation Based on General Regression Neural Network , 2016 .

[10]  Dafeng Jin,et al.  Road condition estimation for automotive anti-skid control system based on BP neural network , 2005, IEEE International Conference Mechatronics and Automation, 2005.

[11]  Huei Peng,et al.  Road Friction Coefficient Estimation For Vehicle Path Prediction , 1996 .

[12]  Hsin Guan,et al.  Road surface condition identification approach based on road characteristic value , 2014 .

[13]  Edoardo Sabbioni,et al.  On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results , 2011 .

[14]  Aldo Sorniotti,et al.  On the experimental analysis of single input single output control of yaw rate and sideslip angle , 2017 .

[15]  Aldo Sorniotti,et al.  Torque Vectoring for Electric Vehicles with Individually Controlled Motors: State-of-the-Art and Future Developments , 2012 .

[16]  Julien Caroux,et al.  SIDESLIP ANGLE MEASUREMENT, EXPERIMENTAL CHARACTERIZATION AND EVALUATION OF THREE DIFFERENT PRINCIPLES , 2007 .

[17]  Hideaki Sasaki,et al.  A Side-Slip Angle Estimation Using Neural Network for a Wheeled Vehicle , 2000 .

[18]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[19]  Kazuhiko Shimada,et al.  IMPROVEMENT OF VEHICLE MANEUVERABILITY BY DIRECT YAW MOMENT CONTROL. , 1992 .

[20]  K. Gnana Sheela,et al.  Review on Methods to Fix Number of Hidden Neurons in Neural Networks , 2013 .

[21]  Zanten Van,et al.  Control Aspects of the Bosch-VDC , 1996 .

[22]  Huei Peng,et al.  Robust Vehicle Sideslip Angle Estimation Through a Disturbance Rejection Filter That Integrates a Magnetometer With GPS , 2014, IEEE Transactions on Intelligent Transportation Systems.

[23]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[24]  Michael Glenn Fodor,et al.  Active control of vehicle dynamics , 1998, 17th DASC. AIAA/IEEE/SAE. Digital Avionics Systems Conference. Proceedings (Cat. No.98CH36267).

[25]  Giulio Panzani,et al.  Robust Vehicle Sideslip Estimation Based on Kinematic Considerations , 2017 .

[26]  David Crolla,et al.  A review of yaw rate and sideslip controllers for passenger vehicles , 2007 .

[27]  Valérie Renaudin,et al.  Step Length Estimation Using Handheld Inertial Sensors , 2012, Sensors.

[28]  Haruo Shimosaka,et al.  Study on a four wheel steering vehicle driven at an objective side slip angle , 1994 .

[29]  Youngseop Son,et al.  Slip Angle Estimation: Development and Experimental Evaluation , 2013 .

[30]  Ville Viikari,et al.  Road-Condition Recognition Using 24-GHz Automotive Radar , 2009, IEEE Transactions on Intelligent Transportation Systems.

[31]  Kazuaki Terashima,et al.  A survey of technical trend of ADAS and autonomous driving , 2014, Proceedings of Technical Program - 2014 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA).

[32]  Bo-Chiuan Chen,et al.  Sideslip angle estimation using extended Kalman filter , 2008 .